“Files Used:”
../../Data/processed/MMSD_Interceptor_Cases_2_7_22.csv
../../Data/processed/LIMSWasteData_02-09-22.csv
SizeUsed = 1
alphaUsed = .9
N1ShapeUnit = 4
N2ShapeUnit = 5
HeightJit <- 0
WidthJit <- 1
PlotlyConfig <- c("zoomIn2d", "zoomOut2d","lasso2d",
"select2d", "autoScale2d")
MadDF <- FullDF%>%
filter(Site=="Madison")%>%
TrendSDOutlierFilter("N1", 1.5, 13, n = 5, TrendFunc = LoessSmoothMod,
outCol = "FlaggedOutliersN1")%>%
TrendSDOutlierFilter("N2", 1.5, 13, n = 5, TrendFunc = LoessSmoothMod,
outCol = "FlaggedOutliersN2")%>%
mutate(NoOutlierVarN1 = ifelse(FlaggedOutliersN1, NA, N1),
NoOutlierVarN2 = ifelse(FlaggedOutliersN2, NA, N2))
NonOuliersDF <- MadDF%>%
mutate(Outlier = ifelse(FlaggedOutliersN1,N1,NA))%>%
mutate(N1 = NoOutlierVarN1,
N2 = NoOutlierVarN2)
OutLierPlotDF <- MadDF%>%
mutate(OutlierN1 = ifelse(FlaggedOutliersN1,N1,NA),
OutlierN2 = ifelse(FlaggedOutliersN2,N2,NA))%>%
mutate(N1 = NoOutlierVarN1,
N2 = NoOutlierVarN2)%>%
#filter(!(is.na(N1)&is.na(Outlier)))%>%
ggplot(aes(x=Date))+#Data depends on time
geom_jitter(aes(y=N1, color="inlier", info = N1),
data=NonOuliersDF, size = .5, shape = N1ShapeUnit, stroke = .2,
width = WidthJit ,height = HeightJit )+
geom_jitter(aes(y=N2, color="inlier", info = N2),
data=NonOuliersDF, size = .5, shape = N2ShapeUnit, stroke = .2,
width = WidthJit ,height = HeightJit )+
geom_jitter(aes(y=OutlierN1, color="Outlier", info = OutlierN1),
shape=N1ShapeUnit, width = WidthJit ,height = HeightJit )+
geom_jitter(aes(y=OutlierN2, color="Outlier", info = OutlierN2),
shape=N2ShapeUnit, width = WidthJit ,height = HeightJit )+
theme_light() +
#scale_y_log10()+
scale_color_manual(values=c("#F8766D","#999999"))+
ylab("Covid partical conventration (GC/L)")
ggplotly(OutLierPlotDF, tooltip = c("x","y","colour"))%>%
config(modeBarButtonsToRemove = PlotlyConfig)%>%
config(displaylogo = FALSE)ggplotly(OutLierPlotDF+
scale_y_continuous(limits = c(0, 5800000))
, tooltip = c("x","y","colour"))%>%
config(modeBarButtonsToRemove = PlotlyConfig)%>%
config(displaylogo = FALSE)#"2021-06-08","2021-10-17","2021-05-02","2021-01-26"IntercepterDF <- FullDF %>%
group_by(Site)%>%
mutate(FudgeFactor = mean(N1))%>%#Mean of sites to see if it works as normalizer
filter(Site != "Madison")
RotSpacing <- seq(15, 375, length = 6)#Getting equal spaced colors
HueSpace <- hcl(h = RotSpacing, l = 65, c = 100)[1:5]#converting degrees to color
HueSpace <- c(tail(HueSpace, -3), head(HueSpace, 3))#Rotating so P18 has best color
HueSpace[2] <- "#000000" #Changing P18 to blac k
IntercepterOverLay <- IntercepterDF%>%
filter(Date>mdy("1/1/2021"))%>%
ggplot(aes(x=Date))+
geom_point(aes(y=N1,color = Site),size = SizeUsed, alpha= alphaUsed,shape=N1ShapeUnit)+
geom_point(aes(y=N2,color = Site),size = SizeUsed, alpha= alphaUsed,shape=N2ShapeUnit)+
theme_light()+
scale_colour_manual(values = HueSpace)+
scale_y_log10()+
ylab("Covid partical conventration (GC/L)")
ggplotly(IntercepterOverLay)%>%
config(modeBarButtonsToRemove = PlotlyConfig)%>%
config(displaylogo = FALSE)#Get P18 to pop outIntercepterChangeDF <- IntercepterDF%>%
filter(!is.na(N1))%>%
mutate(ChangeN1 = lead(N1) - N1,
ChangeN2 = lead(N2) - N2,
PerChangeN1 = log(lead(N1) - N1),
PerChangeN2 = log(lead(N2) - N2))
IntercepterChangeOverLay <- IntercepterChangeDF%>%
ggplot(aes(x=Date))+
geom_point(aes(y = ChangeN1, color = Site), size = SizeUsed,
alpha = alphaUsed, shape=N1ShapeUnit)+
geom_point(aes(y = ChangeN2,color = Site), size = SizeUsed,
alpha = alphaUsed, shape = N2ShapeUnit)+
scale_colour_manual(values = HueSpace)+
theme_light()+
ylab("gene concentration change (GC/L)")
ggplotly(IntercepterChangeOverLay)%>%
config(modeBarButtonsToRemove = PlotlyConfig)%>%
config(displaylogo = FALSE)IntercepterPerChangeOverLay <- IntercepterChangeDF%>%
ggplot(aes(x = Date))+
geom_point(aes(y = PerChangeN1, color = Site), size = SizeUsed,
alpha = alphaUsed, shape = N1ShapeUnit)+
geom_point(aes(y = PerChangeN2, color = Site), size = SizeUsed,
alpha= alphaUsed,shape = N2ShapeUnit)+
theme_light()#+
#scale_y_log10()
#ggplotly(IntercepterPerChangeOverLay)MadisonChangeDF <- FullDF%>%
filter(Site=="Madison",!is.na(N1))%>%
mutate(ChangeN1 = lead(N1) - N1,
ChangeN2 = lead(N2) - N2,
PerChangeN1 = log(lead(N1) - N1),
PerChangeN2 = log(lead(N2) - N2))
IntercepterChangeOverLay <- MadisonChangeDF%>%
ggplot(aes(x=Date))+
geom_point(aes(y = ChangeN1, color = Site), size = SizeUsed,
alpha = alphaUsed, shape=N1ShapeUnit)+
geom_point(aes(y = ChangeN2,color = Site), size = SizeUsed,
alpha = alphaUsed, shape = N2ShapeUnit)+
scale_colour_manual(values = HueSpace)+
theme_light()+
ylab("gene concentration change (GC/L)")
ggplotly(IntercepterChangeOverLay)%>%
config(modeBarButtonsToRemove = PlotlyConfig)%>%
config(displaylogo = FALSE)IntercepterPerChangeOverLay <- MadisonChangeDF%>%
ggplot(aes(x = Date))+
geom_point(aes(y = PerChangeN1, color = Site), size = SizeUsed,
alpha = alphaUsed, shape = N1ShapeUnit)+
geom_point(aes(y = PerChangeN2, color = Site), size = SizeUsed,
alpha= alphaUsed,shape = N2ShapeUnit)+
theme_light()#+
#scale_y_log10()
#ggplotly(IntercepterPerChangeOverLay)
#Flag outliers in graphicLoessFunc <- function(SiteFilter,DF,SpanConstant = .163,Var){
MainDF <- DF%>%
filter(Site==SiteFilter)
MainDF[[paste0("loess",Var)]] <- loessFit(y=(MainDF[[Var]]),
x=MainDF$Date, #create loess fit of the data
span=SpanConstant, #span of .2 seems to give the best result,
#not rigorously chosen
iterations=5)$fitted#2 iterations remove some bad patterns
#Same as above but for N2
N2Name <- gsub("1","2",Var)
MainDF[[paste0("loess", N2Name)]] <- loessFit(y=(MainDF[[N2Name]]),
x=MainDF$Date,
span=SpanConstant,
iterations=5)$fitted#
return(MainDF)
}
#Temp clone to test 7 MA
# LoessFunc <- function(SiteFilter,DF,SpanConstant = .163,Var){
# N2Name <- gsub("1","2",Var)
# MainDF <- DF%>%
# filter(Site==SiteFilter)%>%
# mutate(!!paste0("loess",Var) := rollapply(!!sym(Var), width = 51,
# FUN = mean, fill = NA, na.rm = TRUE ),
# !!paste0("loess",N2Name) := rollapply(!!sym(N2Name), width = 51,
# FUN = mean, fill = NA, na.rm = TRUE ))
# return(MainDF)
# }
SiteLoessDF <- IntercepterDF%>%
mutate(FlowN1 = FlowRate*N1,
PopN1 = N1/Pop,
FlowN2 = FlowRate*N2,
PopN2 = N1/Pop)
BaseColDFN1 <- lapply(c("MMSD-P11","MMSD-P18","MMSD-P2","MMSD-P7","MMSD-P8"),
LoessFunc,SiteLoessDF,SpanConstant = .15,
Var = "N1")%>%
bind_rows()%>%
mutate(Norm = "None",
CovConcNorm = loessN1,
Messure = "N1")
FlowColDFN1 <- lapply(c("MMSD-P11","MMSD-P18","MMSD-P2","MMSD-P7","MMSD-P8"),
LoessFunc,SiteLoessDF,SpanConstant = .15,
Var = "FlowN1")%>%
bind_rows()%>%
mutate(Norm = "Flow",
CovConcNorm = loessFlowN1,
Messure = "N1")
PopColDFN1 <- lapply(c("MMSD-P11","MMSD-P18","MMSD-P2","MMSD-P7","MMSD-P8"),
LoessFunc,SiteLoessDF,SpanConstant = .15,
Var = "PopN1")%>%
bind_rows()%>%
mutate(Norm = "Pop",
CovConcNorm = loessPopN1,
Messure = "N1")
BaseColDFN2 <- BaseColDFN1%>%
mutate(Norm = "None",
CovConcNorm = loessN2,
Messure = "N2")
FlowColDFN2 <- FlowColDFN1%>%
mutate(Norm = "Flow",
CovConcNorm = loessFlowN2,
Messure = "N2")
PopColDFN2 <- PopColDFN1%>%
mutate(Norm = "Pop",
CovConcNorm = loessPopN2,
Messure = "N2")
MergedToBeFacetedDF <- rbind(BaseColDFN1, FlowColDFN1, PopColDFN1,
BaseColDFN2, FlowColDFN2, PopColDFN2)%>%
mutate(Norm = factor(Norm,c("None","Flow","Pop")))%>%
select(Norm,CovConcNorm,Messure,Date)
BaseGridPlot <- MergedToBeFacetedDF%>%
filter(!is.na(CovConcNorm))%>%
ggplot(aes(x=Date))+
geom_line(aes(y=CovConcNorm, color = Site))+
scale_colour_manual(values = HueSpace)+
theme_light() +
facet_grid(Norm~Messure, scales = "free_y") +
theme(panel.spacing = unit(2, "lines"))+
ylab("gene concentration with Normalization (GC/L)")
NonLogPlotlyPlot <- ggplotly( BaseGridPlot
)%>%
config(modeBarButtonsToRemove = PlotlyConfig,
displaylogo = FALSE)
LogPlotlyPlot <- ggplotly( BaseGridPlot+
scale_y_log10())%>%
config(modeBarButtonsToRemove = PlotlyConfig,
displaylogo = FALSE)
NonLogPlotlyPlotLogPlotlyPlot#BaseGridPlot
#BaseGridPlot+
#scale_y_log10()Mean <- 11.73
StandardDeviation <- 7.68
Scale = StandardDeviation^2/Mean
Shape = Mean/Scale
SLDWidth <- 21
weights <- dgamma(1:SLDWidth, scale = Scale, shape = Shape)
SiteLoessDF <- FullDF%>%
filter(Site!="Madison")
A <- SiteLoessDF%>%
filter(!is.na(SevenDayMACases),
SevenDayMACases != 0)%>%
ggplot(aes(x=Date))+
geom_point(aes(y = FirstConfirmed, color = Site),
data = IntercepterDF, size = .5, alpha = .5)+
#geom_line(aes(y = SevenDayMACases, color = Site))+
theme_light() +
scale_colour_manual(values = HueSpace)+
geom_line(aes(y = SLDCases, color = Site))+
scale_y_log10()
ggplotly(A)%>%
config(modeBarButtonsToRemove = PlotlyConfig)%>%
config(displaylogo = FALSE)B <- SiteLoessDF%>%
filter(!is.na(SevenDayMACases),
SevenDayMACases != 0)%>%
ggplot(aes(x=Date))+
geom_point(aes(y = 10000 * FirstConfirmed / Pop, color = Site),
data = IntercepterDF, size = .5, alpha = .5)+
geom_line(aes(y = 10000 * SLDCases / Pop, color = Site))+
#geom_line(aes(y = SevenDayMACases / Pop, color = Site))+
theme_light() +
scale_colour_manual(values = HueSpace)+
scale_y_log10()+
ylab("Cases per 10,000 people")
ggplotly(B)%>%
config(modeBarButtonsToRemove = PlotlyConfig,
displaylogo = FALSE)